Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes

Abstract We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from...

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Autores principales:	Kimberly E. Roche, Marvin Weinstein, Leland J. Dunwoodie, William L. Poehlman, Frank A. Feltus
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5df7caa6fa724781984fe8b965d77763
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spelling	oai:doaj.org-article:5df7caa6fa724781984fe8b965d777632021-12-02T11:40:25ZSorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes10.1038/s41598-018-26310-x2045-2322https://doaj.org/article/5df7caa6fa724781984fe8b965d777632018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-26310-xhttps://doaj.org/toc/2045-2322Abstract We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn’t detectable with the core biomarker transcripts present. This suggests the existence of a “background classification” potential in which the pattern of gene expression after continued removal of “biomarker” transcripts could still classify tumors in agreement with the tumor type.Kimberly E. RocheMarvin WeinsteinLeland J. DunwoodieWilliam L. PoehlmanFrank A. FeltusNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kimberly E. Roche Marvin Weinstein Leland J. Dunwoodie William L. Poehlman Frank A. Feltus Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
description	Abstract We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn’t detectable with the core biomarker transcripts present. This suggests the existence of a “background classification” potential in which the pattern of gene expression after continued removal of “biomarker” transcripts could still classify tumors in agreement with the tumor type.
format	article
author	Kimberly E. Roche Marvin Weinstein Leland J. Dunwoodie William L. Poehlman Frank A. Feltus
author_facet	Kimberly E. Roche Marvin Weinstein Leland J. Dunwoodie William L. Poehlman Frank A. Feltus
author_sort	Kimberly E. Roche
title	Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
title_short	Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
title_full	Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
title_fullStr	Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
title_full_unstemmed	Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
title_sort	sorting five human tumor types reveals specific biomarkers and background classification genes
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/5df7caa6fa724781984fe8b965d77763
work_keys_str_mv	AT kimberlyeroche sortingfivehumantumortypesrevealsspecificbiomarkersandbackgroundclassificationgenes AT marvinweinstein sortingfivehumantumortypesrevealsspecificbiomarkersandbackgroundclassificationgenes AT lelandjdunwoodie sortingfivehumantumortypesrevealsspecificbiomarkersandbackgroundclassificationgenes AT williamlpoehlman sortingfivehumantumortypesrevealsspecificbiomarkersandbackgroundclassificationgenes AT frankafeltus sortingfivehumantumortypesrevealsspecificbiomarkersandbackgroundclassificationgenes
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Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes

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